System Identification Techniques for Soft Sensors and Multiphase Flow Metering

Abstract

Production rates estimation and forecasting is a complex problem in multiphase flow systems, such as oil wells. In the oil and gas industry, this task is normally performed by first principles simulation models, which are costly to maintain and adjust. An alternative to this process could be black-box models based on historical data to perform this task. This work proposes to perform an extensive evaluation of existing regressors to create a black-box system identification model to predict oil well liquid flow rates. Improvement strategies such as the creation of error models, time series decomposition, and the use of current-time data are also evaluated. The final results demonstrated the importance of pressure and temperature data for the well flow rate determination and identified the use of current-time data as the best tested technique to improve the performance of the final model.